Open AccessAn Energy Harvesting Roadside Unit communication load prediction and energy scheduling based on graph convolutional neural networks for spatial‐temporal vehicle data
Author(s) -
Ding Xu,
Zheng Hang,
Wang Yang,
Zhao Chong,
Yang Fan
Publication year - 2022
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12149
Subject(s) - computer science , leverage (statistics) , scheduling (production processes) , software deployment , wireless ad hoc network , efficient energy use , convolutional neural network , real time computing , energy harvesting , energy consumption , distributed computing , artificial intelligence , computer network , energy (signal processing) , wireless , telecommunications , mathematical optimization , statistics , mathematics , electrical engineering , engineering , operating system , ecology , biology
The Energy Harvesting Roadside Unit (EH‐RSU) with self‐powered module will not only effectively reduce the communication load of regional Vehicular Ad Hoc Networks, but also enjoys a low deployment cost. Given the imbalance in communication demands invoked by transportation systems, the EH‐RSU should allocate energy appropriately in accordance with its energy harvesting rate to ensure the communication safety of vehicles within its coverage. Firstly, we propose a novel attention‐based spatial‐temporal graph convolutional network (ASTGCN) to predict the communication load around the EH‐RSU in the road network through the surrounding vehicle information. Secondly, we use the predicted communication load as part of the input parameters to neural network and leverage a double deep Q network to ameliorate the operating states switching strategy of EH‐RSUs by reinforcement learning so that they achieve a more satisfying effective time with limited resources. Finally, we built a dataset by simulation to validate the effectiveness of our model. The results show that our prediction model has a better accuracy and the improved strategy has higher efficiency compared with other methods.